Übersetzung für "Conditional probabilities" in Deutsch

The Naive Bayes Classifier attempts to make predictions on the basis of conditional probabilities.

He lives and breathes stochastic processes and conditional probabilities.

In contrast of the entropy, the entropy cover incorporates conditional probabilities and it's value is invariant against basis transformations.

So I think those babies are actually making complicated calculations with conditional probabilities that they're revising to figure out how the world works.

The information to be taken into account is, for instance, made available in the form of tables using conditional probabilities, so-called Markov chains, for example.

These conditional probabilities are “a-posteriori probabilities”, since the probabilities of the uncoded data symbols (here: bits) u 1 to u N on which an event (the detected sequences {circumflex over (X)}, ? 1, Z) which has occurred is based are deduced from this event.

The negative logarithms of these conditional probabilities are then the distance measures used for the recognition.

Subsequently, the smallest and the largest standardized frequency of all prototype reference values are determined for each time one phoneme, whereupon a scaling factor Sj is determined, which ensures that the values of all the conditional probabilities p'(1/j) or the logarithms have a constant range of values.

Usually, the conditional probabilities presented within text books have been derived from natural frequencies.

The probability of an intersection of events is calculated by the multiplicative rule, which makes use of conditional probabilities.

It is possible to use so-called Markov networks for modeling conditional probabilities in different applications of the artificial intelligence.

An interaction of equipment parameters in the first correlation can thus correspond to an interaction of equipment parameters in the second correlation as a result of which the conditional probabilities corresponding to the correlations can initially be equal for both outer colors.

The transition probabilities P 304, P 305 and P 306 can thus represent conditional probabilities in the case of which the occurrence of the state FK 302 is already presupposed.

Similarly, the transition probabilities P 307 and P 308 can thus represent conditional probabilities in the case of which the occurrence of the state FK 303 is presupposed.

In the case of the Markov network, conditional probabilities of dependent characteristics (e.g. DSG transmission, rocker switches at the steering wheel, etc.) can then also be transitively calculated or corrected in an ongoing illustrative embodiment.

The conditional probabilities can thus be, e.g., transition probabilities starting from a vehicle configuration (FK 1) characterized by the first parameter.

The conditional probabilities in the matrix A are intended, in particular, to express the fact that, for example, a direct transition from fatigue level 1 “awake” to fatigue level 3, “fatigued” is considerably less probable than a direct transition from level 1 to level 2, “tired”, in which, in comparison to level 1, initial adverse effects in terms of the attention of the driver can now be identified.

An indefinite number of fatigue levels can now be defined in the next method step S 5, and conditional probabilities between these levels as well as the observed steering inattentiveness event can now be allocated.

Conditional probabilities can then likewise be allocated, also in the method step S 5, in the form of a matrix B between each of these fatigue levels and one of the extent levels described above.

These conditional probabilities are preferably predetermined in a suitable manner in the form of a matrix A in the course of the probabilistic model.

The configuration of the matrix A should also take account of the fact that the transitions from being awake to a state of greater fatigue take place with different conditional probabilities, in the same way as the transitions from a fatigued state to being awake.

The weights for the conditional probabilities are defined by the probabilities of the conditional events B and not B.

Conditional probabilities of encountering foxes were substantially higher in gardens, areas with a low building density, parks or squares as compared to agricultural areas, industrial areas or forests.

The probability of the event A is the weighted average of the conditional probabilities of A given B, and A given not B.

Because of this, a potential analysis can be used to derive conditional probabilities for a great number of results, especially in regional comparison.